Best of Data Visualization — July 2024

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    Video
    Avatar of TechWithTimTech With Tim·2y

    Master Python With This ONE Project!

    This post guides you through building a personal finance tracker in Python, covering syntax, advanced features, and popular modules like Pandas and Matplotlib. The project involves tracking and logging transactions, organizing data, generating summaries of income and expenses, and visualizing the data with graphs. It also explains how to use CSV files for data storage and offers a quick demo followed by step-by-step instructions for implementation.

  2. 2
    Article
    Avatar of kdnuggetsKDnuggets·2y

    Building Data Science Pipelines Using Pandas

    Learn to build end-to-end data science pipelines using the Pandas pipe method. This method enhances code readability, enables function chaining, and improves code organization. The tutorial includes transforming code into a pipeline structure that handles data ingestion, cleaning, analysis, and visualization, demonstrating a comparison between pipeline and non-pipeline approaches.

  3. 3
    Article
    Avatar of communityCommunity Picks·2y

    Build a Chatbot for your SQL database in 20 lines of Python using Streamlit and Vanna

    Learn how to build a chatbot for your SQL database using Streamlit and Vanna in just 20 lines of Python. The guide walks through setting up the environment, connecting to a SQLite database, generating SQL queries with AI, and visualizing the results in tables and charts.

  4. 4
    Article
    Avatar of kdnuggetsKDnuggets·2y

    5 Tools Every Data Scientist Needs in Their Toolbox in 2024

    To excel in data science in 2024, it's crucial to have the right tools: Python for programming, a solid foundation in maths and statistics, data visualization tools like Matplotlib and Tableau, SQL for managing databases, and frameworks such as TensorFlow and PyTorch. These tools help streamline your workflow and improve your ability to extract and communicate insights effectively.

  5. 5
    Article
    Avatar of juliablJulia Bloggers·2y

    Diagramming + Data Visualization with Julia

    Vizagrams.jl is a Julia package that blends diagramming and data visualization. It offers a diagramming Domain Specific Language (DSL) with a data visualization grammar built on top of it, inspired by the Haskell library Diagrams. The package allows for creating complex visualizations by combining primitive graphical objects and applying stylistic and geometric transformations. It supports the creation of visualizations similar to those produced by Vega-Lite and can be integrated into notebooks like Jupyter or Pluto for interactive experimentation.

  6. 6
    Article
    Avatar of kdnuggetsKDnuggets·2y

    7 Steps to Master the Art of Data Storytelling

    Learn the 7 essential steps to master the art of data storytelling. From defining your story and understanding your audience to collecting and analyzing data, building a narrative, visualizing insights, and effectively communicating your story, these steps will help you turn raw data into engaging, informative, and actionable insights.

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    Article
    Avatar of communityCommunity Picks·2y

    A Quick-ish Accessibility Review: shadcn/ui Charts

    A review of the accessibility of shadcn/ui Charts reveals multiple critical issues. Claims of 'screen reader support' are found to be false, with no data presented to screen readers during navigation. The documentation lacks details on expected keyboard and screen reader interactions, undermining the library's accessibility claims. The review stresses the importance of genuinely addressing accessibility features in popular libraries, emphasizing that current claims appear performative and do not inspire trust.

  8. 8
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·2y

    Improve Matplotlib Plot Quality

    Matplotlib plots in Jupyter Notebook can appear dull and blurry when scaled. A useful hack is to render plots as SVGs (Scalable Vector Graphics) instead of the default image format. This ensures high-quality plots that remain sharp even when zoomed. Use either `from matplotlib_inline.backend_inline import set_matplotlib_formats` with `set_matplotlib_formats('svg')` or `%config InlineBackend.figure_format = 'svg'` to implement this improvement in your notebooks.

  9. 9
    Article
    Avatar of streamlitStreamlit·2y

    Streamlit 101: The fundamentals of a Python data app

    Streamlit is an open-source Python framework designed to simplify the creation of highly interactive data apps. It features intuitive syntax that eliminates the need for CSS, HTML, or JavaScript, seamless integration with various data libraries and AI frameworks, and quick iteration cycles. A typical Streamlit setup involves loading data, creating visual components like bar charts and line charts with built-in interactivity, and easy deployment options ranging from local environments to server and cloud deployments. GitHub Codespaces can be used to get started quickly with Streamlit, and the community provides numerous reusable components.

  10. 10
    Article
    Avatar of awelixAwesome Elixir·2y

    Data Visualization for Machine Learning in Elixir

    Explores the techniques for data visualization in machine learning using Elixir, utilizing libraries such as VegaLite and Axon. It includes methods for setting up dependencies, creating scatter plots, facetted scatter plots, and multiple scatter plots with the Iris dataset. Additionally, it demonstrates how to track and plot training metrics like loss and accuracy, both post-training and in real-time. The post concludes with a look into Axon's native plotting capabilities and hints at further exploration in Elixir's ML ecosystem.

  11. 11
    Article
    Avatar of kdnuggetsKDnuggets·2y

    Tools Every Data Scientist Should Know: A Practical Guide

    Discover the essential tools for data scientists, from programming languages like Python and R to SQL, Excel, and advanced visualization platforms. Learn about key libraries for web scraping, data manipulation, and model building in Python and R. Understand the importance of cloud systems like AWS and Google Cloud, and explore advanced visualization tools such as Power BI and Tableau. Additionally, get insights on Large Language Models (LLMs) like ChatGPT and Microsoft's Co-pilot, which are revolutionizing AI applications.

  12. 12
    Article
    Avatar of planetpythonPlanet Python·2y

    PyCoder’s Weekly

    Learn to create GUI applications with Python and PyQt by building a desktop calculator in a new video course. Umbra Space's new dataset offers satellite-based radar images for visualizing and annotating shipping data. Pydantic's new observability platform, Logfire, helps monitor your app with ease. Explore modern good practices for Python development and check out tutorials on building a guitar synthesizer using Python, understanding Python's security model, and creating high-quality README files for your projects.

  13. 13
    Article
    Avatar of communityCommunity Picks·2y

    Chat with Neon Postgres using natural language

    AskYourDatabase leverages GPT-4 to enable natural language interaction with SQL databases. It can handle data analysis, CRUD operations, data visualization, and schema migrations without requiring SQL expertise. This tool is especially useful for non-technical team members and can integrate with Neon's branching feature to safely manage database queries and updates in a development environment. Integration is simple: just download the app and connect it to your Neon dev branch. A special discount is available for Neon users who email for more information.

  14. 14
    Article
    Avatar of hnHacker News·2y

    finos/perspective: A data visualization and analytics component, especially well-suited for large and/or streaming datasets.

    Perspective is an interactive analytics and data visualization component designed for large and streaming datasets. It includes a fast, memory-efficient streaming query engine written in C++ and compiled for WebAssembly and Python. The framework-agnostic interface can run in-browser or via WebSocket server, and it provides a JupyterLab widget and Python client library for interactive data analysis.

  15. 15
    Article
    Avatar of neo4jneo4j·2y

    Visualizing Graph Data with Neo4j Bloom (ScoobyGraph, Part 3)

    This post explores how to visualize graph data using Neo4j Bloom, particularly focusing on a Scooby Doo graph database. It covers the creation and formatting of graph visualizations, adding categories and relationships, and utilizing graph pattern searches and custom queries. Additionally, the post discusses how to filter, animate, and expand the graph to uncover deeper insights. Instructions for importing pre-built perspectives and employing custom scene actions are also included.

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